For instance, imagine that you are coding in any text editor like Windows notepad. So why would or do you choose a traditional IDE instead of, for example, a notepad? The truth is that you can code in almost any software, from prompt command to Windows notepad, but you may also want a proper programming environment which combines coding facility with a debugging environment.

However, given the fact that there aren’t only the traditional IDEs to consider, but also new tools, such as notebooks, you might be wondering which development environment to use when you’re just starting out with data science. You can probably most clearly see this evolution in the results of the Stack Overflow Developer Survey below, which also includes these new tools, next to the traditional IDEs that you might already know They all fall under the section “development environment”.īecause of all the features that IDEs have to offer, they are extremely useful for development: they make your coding more comfortable and this is no different for data science. For example, debugging your code is also possible in Jupyter Notebook. It’s a coding tool which allows you to write, test, and debug your code in an easier way, as they typically offer code completion or code insight by highlighting, resource management, debugging tools,… And even though the IDE is a strictly defined concept, it’s starting to be redefined as other tools such as notebooks start gaining more and more features that traditionally belong to IDEs. IDE stands for Integrated Development Environment.

Check out our new Top Python IDEs for 2019 tutorial.

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